from qiita_db.util import MaxRSS_helper
from qiita_db.software import Software
import datetime
from io import StringIO
from subprocess import check_output
import pandas as pd
from os.path import join
# This is an example script to collect the data we need from SLURM, the plan
# is that in the near future we will clean up and add these to the Qiita's main
# code and then have cronjobs to run them.
# at time of writting we have:
# qp-spades spades
# (*) qp-woltka Woltka v0.1.4
# qp-woltka SynDNA Woltka
# qp-woltka Calculate Cell Counts
# (*) qp-meta Sortmerna v2.1b
# (*) qp-fastp-minimap2 Adapter and host filtering v2023.12
# ... and the admin plugin
# (*) qp-klp
# Here we are only going to create summaries for (*)
sacct = ['sacct', '-p',
'--format=JobName,JobID,ElapsedRaw,MaxRSS,ReqMem', '-j']
# for the non admin jobs, we will use jobs from the last six months
six_months = datetime.date.today() - datetime.timedelta(weeks=6*4)
print('The current "sofware - commands" that use job-arrays are:')
for s in Software.iter():
if 'ENVIRONMENT="' in s.environment_script:
for c in s.commands:
print(f"{s.name} - {c.name}")
# 1. Command: woltka
fn = join('/panfs', 'qiita', 'jobs_woltka.tsv.gz')
print(f"Generating the summary for the woltka jobs: {fn}.")
cmds = [c for s in Software.iter(False)
if 'woltka' in s.name for c in s.commands]
jobs = [j for c in cmds for j in c.processing_jobs if j.status == 'success' and
j.heartbeat.date() > six_months and j.input_artifacts]
data = []
for j in jobs:
size = sum([fp['fp_size'] for fp in j.input_artifacts[0].filepaths])
jid, mjid = j.external_id.strip().split()
rvals = StringIO(check_output(sacct + [jid]).decode('ascii'))
_d = pd.read_csv(rvals, sep='|')
jmem = _d.MaxRSS.apply(lambda x: x if type(x) is not str
else MaxRSS_helper(x)).max()
jwt = _d.ElapsedRaw.max()
rvals = StringIO(check_output(sacct + [mjid]).decode('ascii'))
_d = pd.read_csv(rvals, sep='|')
mmem = _d.MaxRSS.apply(lambda x: x if type(x) is not str
else MaxRSS_helper(x)).max()
mwt = _d.ElapsedRaw.max()
data.append({
'jid': j.id, 'sjid': jid, 'mem': jmem, 'wt': jwt, 'type': 'main',
'db': j.parameters.values['Database'].split('/')[-1]})
data.append(
{'jid': j.id, 'sjid': mjid, 'mem': mmem, 'wt': mwt, 'type': 'merge',
'db': j.parameters.values['Database'].split('/')[-1]})
df = pd.DataFrame(data)
df.to_csv(fn, sep='\t', index=False)
# 2. qp-meta Sortmerna
fn = join('/panfs', 'qiita', 'jobs_sortmerna.tsv.gz')
print(f"Generating the summary for the woltka jobs: {fn}.")
# for woltka we will only use jobs from the last 6 months
cmds = [c for s in Software.iter(False)
if 'minimap2' in s.name.lower() for c in s.commands]
jobs = [j for c in cmds for j in c.processing_jobs if j.status == 'success' and
j.heartbeat.date() > six_months and j.input_artifacts]
data = []
for j in jobs:
size = sum([fp['fp_size'] for fp in j.input_artifacts[0].filepaths])
jid, mjid = j.external_id.strip().split()
rvals = StringIO(check_output(sacct + [jid]).decode('ascii'))
_d = pd.read_csv(rvals, sep='|')
jmem = _d.MaxRSS.apply(lambda x: x if type(x) is not str
else MaxRSS_helper(x)).max()
jwt = _d.ElapsedRaw.max()
rvals = StringIO(check_output(sacct + [mjid]).decode('ascii'))
_d = pd.read_csv(rvals, sep='|')
mmem = _d.MaxRSS.apply(lambda x: x if type(x) is not str
else MaxRSS_helper(x)).max()
mwt = _d.ElapsedRaw.max()
data.append({
'jid': j.id, 'sjid': jid, 'mem': jmem, 'wt': jwt, 'type': 'main'})
data.append(
{'jid': j.id, 'sjid': mjid, 'mem': mmem, 'wt': mwt, 'type': 'merge'})
df = pd.DataFrame(data)
df.to_csv(fn, sep='\t', index=False)
# 3. Adapter and host filtering. Note that there is a new version deployed on
# Jan 2024 so the current results will not be the most accurate
fn = join('/panfs', 'qiita', 'jobs_adapter_host.tsv.gz')
print(f"Generating the summary for the woltka jobs: {fn}.")
# for woltka we will only use jobs from the last 6 months
cmds = [c for s in Software.iter(False)
if 'meta' in s.name.lower() for c in s.commands]
jobs = [j for c in cmds if 'sortmerna' in c.name.lower()
for j in c.processing_jobs if j.status == 'success' and
j.heartbeat.date() > six_months and j.input_artifacts]
data = []
for j in jobs:
size = sum([fp['fp_size'] for fp in j.input_artifacts[0].filepaths])
jid, mjid = j.external_id.strip().split()
rvals = StringIO(check_output(sacct + [jid]).decode('ascii'))
_d = pd.read_csv(rvals, sep='|')
jmem = _d.MaxRSS.apply(lambda x: x if type(x) is not str
else MaxRSS_helper(x)).max()
jwt = _d.ElapsedRaw.max()
rvals = StringIO(check_output(sacct + [mjid]).decode('ascii'))
_d = pd.read_csv(rvals, sep='|')
mmem = _d.MaxRSS.apply(lambda x: x if type(x) is not str
else MaxRSS_helper(x)).max()
mwt = _d.ElapsedRaw.max()
data.append({
'jid': j.id, 'sjid': jid, 'mem': jmem, 'wt': jwt, 'type': 'main'})
data.append(
{'jid': j.id, 'sjid': mjid, 'mem': mmem, 'wt': mwt, 'type': 'merge'})
df = pd.DataFrame(data)
df.to_csv(fn, sep='\t', index=False)
# 4. The SPP!
fn = join('/panfs', 'qiita', 'jobs_spp.tsv.gz')
print(f"Generating the summary for the SPP jobs: {fn}.")
# for the SPP we will look at jobs from the last year
year = datetime.date.today() - datetime.timedelta(days=365)
cmds = [c for s in Software.iter(False)
if s.name == 'qp-klp' for c in s.commands]
jobs = [j for c in cmds for j in c.processing_jobs if j.status == 'success' and
j.heartbeat.date() > year]
# for the SPP we need to find the jobs that were actually run, this means
# looping throught the existing slurm jobs and finding them
max_inter = 2000
data = []
for job in jobs:
jei = int(job.external_id)
rvals = StringIO(
check_output(sacct + [str(jei)]).decode('ascii'))
_d = pd.read_csv(rvals, sep='|')
mem = _d.MaxRSS.apply(
lambda x: x if type(x) is not str else MaxRSS_helper(x)).max()
wt = _d.ElapsedRaw.max()
# the current "easy" way to determine if amplicon or other is to check
# the file extension of the filename
stype = 'other'
if job.parameters.values['sample_sheet']['filename'].endswith('.txt'):
stype = 'amplicon'
rid = job.parameters.values['run_identifier']
data.append(
{'jid': job.id, 'sjid': jei, 'mem': mem, 'stype': stype, 'wt': wt,
'type': 'main', 'rid': rid, 'name': _d.JobName[0]})
# let's look for the convert job
for jid in range(jei + 1, jei + max_inter):
rvals = StringIO(check_output(sacct + [str(jid)]).decode('ascii'))
_d = pd.read_csv(rvals, sep='|')
if [1 for x in _d.JobName.values if x.startswith(job.id)]:
cjid = int(_d.JobID[0])
mem = _d.MaxRSS.apply(
lambda x: x if type(x) is not str else MaxRSS_helper(x)).max()
wt = _d.ElapsedRaw.max()
data.append(
{'jid': job.id, 'sjid': cjid, 'mem': mem, 'stype': stype,
'wt': wt, 'type': 'convert', 'rid': rid,
'name': _d.JobName[0]})
# now let's look for the next step, if amplicon that's fastqc but
# if other that's qc/nuqc
for jid in range(cjid + 1, cjid + max_inter):
rvals = StringIO(
check_output(sacct + [str(jid)]).decode('ascii'))
_d = pd.read_csv(rvals, sep='|')
if [1 for x in _d.JobName.values if x.startswith(job.id)]:
qc_jid = _d.JobIDRaw.apply(
lambda x: int(x.split('.')[0])).max()
qcmem = _d.MaxRSS.apply(
lambda x: x if type(x) is not str
else MaxRSS_helper(x)).max()
qcwt = _d.ElapsedRaw.max()
if stype == 'amplicon':
data.append(
{'jid': job.id, 'sjid': qc_jid, 'mem': qcmem,
'stype': stype, 'wt': qcwt, 'type': 'fastqc',
'rid': rid, 'name': _d.JobName[0]})
else:
data.append(
{'jid': job.id, 'sjid': qc_jid, 'mem': qcmem,
'stype': stype, 'wt': qcwt, 'type': 'qc',
'rid': rid, 'name': _d.JobName[0]})
for jid in range(qc_jid + 1, qc_jid + max_inter):
rvals = StringIO(check_output(
sacct + [str(jid)]).decode('ascii'))
_d = pd.read_csv(rvals, sep='|')
if [1 for x in _d.JobName.values if x.startswith(
job.id)]:
fqc_jid = _d.JobIDRaw.apply(
lambda x: int(x.split('.')[0])).max()
fqcmem = _d.MaxRSS.apply(
lambda x: x if type(x) is not str
else MaxRSS_helper(x)).max()
fqcwt = _d.ElapsedRaw.max()
data.append(
{'jid': job.id, 'sjid': fqc_jid,
'mem': fqcmem, 'stype': stype,
'wt': fqcwt, 'type': 'fastqc',
'rid': rid, 'name': _d.JobName[0]})
break
break
break
df = pd.DataFrame(data)
df.to_csv(fn, sep='\t', index=False)
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